Experiences,activities, and personal characteristics as predictors of engagement in STEM-focused summer programs

Out-of-school-time programs for youth that are focused on STEM content are often seen as affording opportunities to increase youth engagement, interest, and knowledge in STEM domains, yet we know relatively little about how youth actually experience such programs. In this article, we explore how experiences and activities employed in the delivery of summer STEM programs are associated with youth engagement during programming, and whether youth characteristics moderate these relationships. Data were collected from 203 youth (ages 10–16) in nine summer programs using multiple methods including video, experience sampling, and surveys. Through the use of cross-classified, multi-level models, we found that youth reported higher engagement in program activities they perceived to be more challenging and relevant, and in activities, they perceived to have more affordances for learning or developing skills. Gender moderated these relationships such that the positive relationships observed among males were muted or nonexistent for girls. We further identify that program activities are differently associated with fostering challenge, relevance, and learning. Findings have implications for out-of-school STEM programming for youth.     

Tweet,and We Shall Find: Using Digital Methods to Locate Participants in Educational Hashtags

Although researchers have discovered a great deal about who uses Twitter for educational purposes, what they post about, when they post and why they participate, there has so far been little work to explore where participants in educational Twitter contexts are located. In this paper, we establish a methodological foundation that can support the exploration of geographical issues in educational Twitter research. We surveyed 46 participants in one educational Twitter hashtag, #michED, to determine where they lived; we then compared these responses to results from three digital methods for geolocating Twitter users (human coding, machine coding and GPS coding) to explore these methods’ affordances and constraints. Human coding of Twitter profiles allowed us to analyze more participants with higher levels of accuracy but also has disadvantages compared to other digital—and traditional—methods. We discuss the additional insights obtained through geolocating #michED participants as well as considerations for using geolocation and other digital methods in educational research.